By leveraging technologies like machine learning and social media analytics, the field of journalism has evolved to sustain a feedback loop that feeds readers precisely what they need.
The technology drive in a realm that was so far guarded against major disruptions has now increased efficiency and productivity and is, therefore, able to deliver news in a manner perceived inconceivable until now.
A tête-à-tête with Ashis Roy, VP-Global Technology Services, Time Inc. India brings to light how journalism has evolved over time, and how the industry leverages cutting edge technologies like cognitive and social media analytics.
As the senior most leader in Time Inc’s India operation, Roy is responsible for the design and development of cutting edge content management system, mobile apps for brands in state of art technologies like live VR, and platform components which scale to support 150 million unique visitors every month.
How is technology actually changing the face of new age journalism?
There are multiple factors in journalism that have been affected by the influx of technology. They are:
“Closely listening to social signals which indicate how readers are responding to articles that are published by sharing it in their social circles, provides valuable feedback on user engagement and interest in the topics published.”
1. Efficiency and Productivity
One of the first ways technology helps is by improving the efficiency and productivity of all journalistic activities. This is done by utilizing real-time data processing to help journalists perform deeper research and identify trends that can grab customer attention.
Technology also enables the use of audio-visual elements in news content, thus widening the reading audience by bringing more forms of media into journalism. Centralized and cloud-based storage provided by technology also enables journalists to work from any location at a faster rate.
2. Distribution Channel
Once the articles have been created, technology aids in dissemination across a variety of media. Moving beyond a print-only mindset, technology enables the same content to be rendered across all devices with different sizes, operating systems, and different capabilities.
Not only this, it allows content to be shared with a specific audience, as well as a global one.
This spread of content, however, also brings with it a question of authenticity, as large volumes of data cannot be easily verified, thus leading to the proliferation of false data sets along with useful ones.
The role of social media analytics in journalism?
Social media analytics is evolving as a critical tool in the media industry. Closely listening to social signals which indicate how readers are responding to articles that are published by sharing it in their social circles, provides valuable feedback on user engagement and interest in the topics published.
Sentiment analysis and studying the reach of the posts along with mining the text of the posts reveals feedback and alternative ideas to encourage voluntary sharing and to guide strategy for future articles and products for any publisher.
Data collection helps build an advanced publisher analytics platform that allows us to draw insights from all types of content and then enhance them appropriately through keywords/entities/sentiments or IAB Taxonomy.
Ashis Roy, VP-Global Technology Services, Time Inc. India
How does Time Inc. incorporate AI and cognitive computing in the media vertical?
Time Inc. is committed to providing the best possible service to its customers and Machine Learning and sophisticated Natural Language Processing Algorithms helps to do just that. Data collection is the first endeavor where this can be seen.
The collected data helps build an advanced publisher analytics platform that allows us to draw insights from all types of content and then enhance them appropriately through keywords/entities/sentiments or IAB Taxonomy.
How machine learning boosts revenue and user engagement?
By making the data more targeted and searchable, editor productivity can also be increased, which simultaneously increases the relevance of all advertisements that are disseminated. This ultimately results in increased revenue and user engagement.
The feedback on the generated content is then gathered using social listening tools along with Natural Language Processing algorithms. This allows for accurate trend identification, which in turn ensures an effective content strategy for the editorial team and better ROI for the advertising department.
Machine learning also aids in getting actionable data on our current and potential subscribers by analyzing the subscription funnel data.
What is Time Inc.’s vision for the Indian market?
Time Inc. is mainly focused on creating and distributing content across North America and the European markets. At the moment, the idea is to observe the Indian market very closely for opportunities.